Enjoy</p>\n","updatedAt":"2026-06-16T07:00:51.397Z","author":{"_id":"62b3a4cf003cd12329e0a822","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62b3a4cf003cd12329e0a822/nZTj3yNcoYlQ2ESCREM0l.jpeg","fullname":"Igor Itkin","name":"BukaByaka","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7659503221511841},"editors":["BukaByaka"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/62b3a4cf003cd12329e0a822/nZTj3yNcoYlQ2ESCREM0l.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2606.14819","authors":[{"_id":"6a30f47ea0d4daae428602f4","name":"Igor Itkin","hidden":false}],"publishedAt":"2026-06-12T00:00:00.000Z","submittedOnDailyAt":"2026-06-16T00:00:00.000Z","title":"Selective Control under Noisy Perception: Governance Failures Hidden by Aggregate Metrics in Modular Networks","submittedOnDailyBy":{"_id":"62b3a4cf003cd12329e0a822","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62b3a4cf003cd12329e0a822/nZTj3yNcoYlQ2ESCREM0l.jpeg","isPro":false,"fullname":"Igor Itkin","user":"BukaByaka","type":"user","name":"BukaByaka"},"summary":"A content-moderation system can score well on every standard accuracy metric and still cause real harm, if its mistakes fall on the few users who connect otherwise separate communities. We show this in an agent-based model where N=240 learning agents on a community-structured network each post harmless, productive, or dangerous content, and a regulator removes or penalizes whatever a noisy classifier flags. Overall usefulness barely moves as the noise changes (one-way ANOVA, p=0.96): by aggregate measures, nothing looks wrong. The damage instead concentrates on these bridge users, whose useful posts are wrongly suppressed and whose dangerous posts are wrongly spared. A governance loss (L_gov) that prices these two mistakes separately from the cost of enforcement more than doubles under false-positive-heavy noise. Aggregate accuracy hides who is harmed, and the cheap quantity to audit is how many connections a user has (degree), a near-perfect proxy for the betweenness that defines a bridge (r=0.96).","upvotes":1,"discussionId":"6a30f47ea0d4daae428602f5","githubRepo":"https://github.com/YehudaItkin/noisy-perception-governance","githubRepoAddedBy":"user","ai_summary":"Content moderation systems can cause disproportionate harm to bridge users connecting separate communities, even when overall accuracy metrics appear satisfactory, with governance loss increasing significantly under false-positive-heavy conditions.","ai_keywords":["agent-based model","community-structured network","noisy classifier","regulator","bridge users","governance loss","false-positive-heavy noise","betweenness","degree"],"ai_summary_model":"Qwen/Qwen2.5-Coder-32B-Instruct","githubStars":0},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"62b3a4cf003cd12329e0a822","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62b3a4cf003cd12329e0a822/nZTj3yNcoYlQ2ESCREM0l.jpeg","isPro":false,"fullname":"Igor Itkin","user":"BukaByaka","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2606/2606.14819.md","query":{}}">
Selective Control under Noisy Perception: Governance Failures Hidden by Aggregate Metrics in Modular Networks
Abstract
Content moderation systems can cause disproportionate harm to bridge users connecting separate communities, even when overall accuracy metrics appear satisfactory, with governance loss increasing significantly under false-positive-heavy conditions.
A content-moderation system can score well on every standard accuracy metric and still cause real harm, if its mistakes fall on the few users who connect otherwise separate communities. We show this in an agent-based model where N=240 learning agents on a community-structured network each post harmless, productive, or dangerous content, and a regulator removes or penalizes whatever a noisy classifier flags. Overall usefulness barely moves as the noise changes (one-way ANOVA, p=0.96): by aggregate measures, nothing looks wrong. The damage instead concentrates on these bridge users, whose useful posts are wrongly suppressed and whose dangerous posts are wrongly spared. A governance loss (L_gov) that prices these two mistakes separately from the cost of enforcement more than doubles under false-positive-heavy noise. Aggregate accuracy hides who is harmed, and the cheap quantity to audit is how many connections a user has (degree), a near-perfect proxy for the betweenness that defines a bridge (r=0.96).
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Cite arxiv.org/abs/2606.14819 in a model README.md to link it from this page.
Cite arxiv.org/abs/2606.14819 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2606.14819 in a Space README.md to link it from this page.
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